Update app.py

app.py

@@ -5,7 +5,6 @@ import joblib
 import pandas as pd
 import os
 
-from gradio.components import Dropdown, Number, Textbox, Column
 
 def load_model():
     cwd = os.getcwd()
@@ -14,16 +13,19 @@ def load_model():
 
     Final_model_file_path = os.path.join(destination, "Final_model.joblib")
     preprocessor_file_path = os.path.join(destination, "preprocessor.joblib")
+    
 
     Final_model = joblib.load(Final_model_file_path)
     preprocessor = joblib.load(preprocessor_file_path)
+    
 
     return Final_model, preprocessor
 
 Final_model, preprocessor = load_model()
 
+
 #define prediction function
-def make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, DATA_VOLUME, ON_NET, ORANGE, TIGO, ZONE1, ZONE2, MRG, REGULARITY, FREQ_TOP_PACK):
+def make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, DATA_VOLUME, ON_NET, ORANGE, TIGO, ZONE1, ZONE2,MRG, REGULARITY, FREQ_TOP_PACK):
     #make a dataframe from input data
     input_data = pd.DataFrame({'REGION':[REGION], 
                                'TENURE':[TENURE], 
@@ -41,48 +43,55 @@ def make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGME
                                'MRG':[MRG],
                                'REGULARITY':[REGULARITY], 
                                'FREQ_TOP_PACK':[FREQ_TOP_PACK]})
-
+    
     transformer = preprocessor.transform(input_data)
-
-    predt = Final_model.predict(transformer)
+   
+    predt = Final_model.predict(transformer) 
     #return prediction
     if predt[0]==1:
         return "Customer will Churn" 
     return "Customer will not Churn"
-
-# Create the input components for gradio
-input_col1 = [Dropdown(choices=['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA', 'KAFFRINE', 'KOLDA']),
-              Dropdown(choices=['K > 24 month', 'I 18-21 month', 'H 15-18 month', 'G 12-15 month', 'J 21-24 month', 'F 9-12 month', 'E 6-9 month', 'D 3-6 month']),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number()]
-
-input_col2 = [Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Number(),
-              Dropdown(choices=['NO']),
-              Number(),
-              Number()]
-
-output = Textbox(label='Prediction')
-
-# Wrap the input components in a Column component
-inputs = Column(children=[input_col1, input_col2], **kwargs={})
-
-# Create the interface component
-app = gr.Interface(fn=make_prediction, inputs=inputs,
-                   title="Customer Churn Predictor",
-                   description="Enter the fields below and click the submit button to Make Your Prediction",
-                   outputs=output)
-
-app.launch(debug=True)
+    
+
+#create the input components for gradio
+REGION = gr.inputs.Dropdown(choices =['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA' 'KAFFRINE,KOLDA', 'FATICK', 'MATAM', 'ZIGUINCHOR', 'SEDHIOU', 'KEDOUGOU']) 
+TENURE = gr.inputs.Dropdown(choices =['K > 24 month', 'I 18-21 month', 'H 15-18 month', 'G 12-15 month', 'J 21-24 month', 'F 9-12 month', 'E 6-9 month', 'D 3-6 month']) 
+MONTANT = gr.inputs.Number()
+FREQUENCE_RECH = gr.Number()
+REVENUE = gr.Number()
+ARPU_SEGMENT = gr.Number() 
+FREQUENCE = gr.Number() 
+DATA_VOLUME = gr.Number() 
+ON_NET = gr.Number()
+ORANGE = gr.Number()
+TIGO = gr.Number()
+ZONE1 = gr.Number() 
+ZONE2 = gr.Number()
+MRG = gr.inputs.Dropdown(choices =['NO'])    
+REGULARITY = gr.Number()
+FREQ_TOP_PACK = gr.Number()
+
+output = gr.Textbox(label='Prediction')
+#create the interface component
+
+app = gr.Interface(fn =make_prediction,inputs =[REGION, 
+                                                TENURE, 
+                                                MONTANT, 
+                                                FREQUENCE_RECH, 
+                                                REVENUE, 
+                                                ARPU_SEGMENT, 
+                                                FREQUENCE, 
+                                                DATA_VOLUME, 
+                                                ON_NET, 
+                                                ORANGE, 
+                                                TIGO, 
+                                                ZONE1, 
+                                                ZONE2,
+                                                MRG, 
+                                                REGULARITY, 
+                                                FREQ_TOP_PACK],
+                                                 title ="Customer Churn Predictor", 
+                                                  description="Enter the feilds Below and click the submit button to Make Your Prediction",
+                                                 outputs = output)
+
+app.launch(share = True, debug = True)